
"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Any=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Any=18							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				str=400							,   UpperCAmelCase_			:				Optional[Any]=True							,   UpperCAmelCase_			:				List[str]=32							,   UpperCAmelCase_			:				Any=True							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		image_size
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize
              UpperCamelCase       			=		size_divisor
              UpperCamelCase       			=		do_rescale






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Dict:







              """simple docstring"""
              return {
                  "do_resize": self.do_resize,
                  "size_divisor": self.size_divisor,
                  "do_rescale": self.do_rescale,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Tuple				=      GLPNImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		GLPNImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size_divisor"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "resample"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_rescale"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Union[str, Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input (GLPNImageProcessor doesn't support batching)
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
              self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Tuple:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input (GLPNImageProcessor doesn't support batching)
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
              self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Tuple:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input (GLPNImageProcessor doesn't support batching)
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
              self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )







"""simple docstring"""


SCREAMING_SNAKE_CASE			  =  {}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""

       # if we are absent twice, or late 3 consecutive days,
       # no further prize strings are possible
       if late == 3 or absent == 2:
              return 0

       # if we have no days left, and have not failed any other rules,
       # we have a prize string
       if days == 0:
              return 1

       # No easy solution, so now we need to do the recursive calculation

       # First, check if the combination is already in the cache, and
       # if yes, return the stored value from there since we already
       # know the number of possible prize strings from this point on
       UpperCamelCase       			=		(days, absent, late)
       if key in cache:
              return cache[key]

       # now we calculate the three possible ways that can unfold from
       # this point on, depending on our attendance today

       # 1) if we are late (but not absent), the "absent" counter stays as
       # it is, but the "late" counter increases by one
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   late + 1					)

       # 2) if we are absent, the "absent" counter increases by 1, and the
       # "late" counter resets to 0
       UpperCamelCase       			=		_calculate(days - 1		,   absent + 1		,   0					)

       # 3) if we are on time, this resets the "late" counter and keeps the
       # absent counter
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   0					)

       UpperCamelCase       			=		state_late + state_absent + state_ontime

       UpperCamelCase       			=		prizestrings
       return prizestrings



def 						lowerCamelCase__      (				UpperCAmelCase_ = 30					)->				int:







       """simple docstring"""
       return _calculate(UpperCAmelCase_		,   absent=0		,   late=0					)


if __name__ == "__main__":
      print(solution())





